We tested reduced precision on the two publicly-available datasets in human activity recognition. The Banos et al. dataset [1]}, [2]} (henceforth referred to as “Banos”) is a human activity
dataset with 17 participants and 9 sensors per participant. Each sensor samples 3D acceleration, gyroscope,
and magnetic field, as well as orientation in a quaternion format, producing a total of 13 values. Sensors
were sampled at 50 Hz, and each sample was associated with one of 33 possible activities. In addition to the
33 activities, an extra activity labelled 0 indicated no specific activity. We preprocessed the Banos dataset
using non-overlapping windows of one second (50 samples) and 6 axes (acceleration and gyroscope of
the right forearm sensor). We computed the average and the standard deviation over the window as features for
each axis and assigned the most frequent label to the window. To ensure that our results were independent of the order of the data points in the dataset, 7 random orderings were tested.
